#   Licensed to the Apache Software Foundation (ASF) under one
#   or more contributor license agreements.  See the NOTICE file
#   distributed with this work for additional information
#   regarding copyright ownership.  The ASF licenses this file
#   to you under the Apache License, Version 2.0 (the
#   "License"); you may not use this file except in compliance
#   with the License.  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed cto in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

# beam-playground:
#   name: Tee
#   description: Task from katas that demonstrates the use of Apache Beam's Tee transform to apply side transformations while preserving the main pipeline flow.
#   multifile: false
#   context_line: 33
#   categories:
#     - Tee
#   complexity: BASIC
#   tags:
#     - tee
#     - transforms
#     - branching

def tee():
  # [START tee]
  import apache_beam as beam

  with beam.Pipeline() as p:
    even_elements = lambda pcoll: pcoll | "Filter Even" >> beam.Filter(lambda x: x % 2 == 0)
    odd_elements = lambda pcoll: pcoll | "Filter Even" >> beam.Filter(lambda x: x % 2 != 0)

    input_data = p | "Create Input" >> beam.Create([1, 2, 3, 4, 5])

    (input_data
      | "Tee Operations" >> beam.Tee(even_elements, odd_elements)
      | "Continue Pipeline" >> beam.Map(lambda x: x * 10)
      | beam.LogElements())
  # [END tee]

if __name__ == '__main__':
  tee()
